to yet to be determined
The group understood the difficulties and significance of the prompt.
What it does
The application reduces human error and resources organizing metabolic data.
How I built it
The application utilizes r to pull scripts from the data APIs, AWS services are used to create the API and transfer the data, and r shiny was used to create the UI.
Challenges I ran into
The DBs did not always output the correct values according to user input. The queries would often choose the wrong compounds. Issues with starting up the environments and pathway errors.
Accomplishments that I'm proud of
It works! It streams data to the user and provides real-time results. We were successful for unfamiliar tools.
What I learned
Most members of the group gained experiences in tools, languages, and environments they had not used before. We learned the limitations and challenges of data collection from databases with limited API functionality.
What's next for MetabulatR
Increasing functionality to display more information for the user. Refactor the code for organization and troubleshooting.